کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
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4962069 | 1446517 | 2016 | 6 صفحه PDF | دانلود رایگان |
Recommendation systems based on collaborative filtering methods can be exploited in the context of providing personalized artworks tours within a museum. However, in order to be effectively used, several problems have to be addressed: user preferences are not expressed as rating, items to be suggested are located in a physical space, and users may be in a group. In this work, we present a general framework that, by using the Matrix Factorization (MF) approach and a graph representation of a museum, addresses the problem of generating and then recommending an artworks sequence for a group of visitors within a museum. To reach a high-quality initial personalization, the recommendation system uses a simple, but efficient, elicitation method that is inspired by the MF approach. Moreover, the proposed approach considers the individual or the aggregated artworks' ratings to build up a solution that takes into account the physical location of the artworks.
Journal: Procedia Computer Science - Volume 98, 2016, Pages 431-436